testZeroes-method: Test for differential proportions of zero gene expression

Description Usage Arguments Details Value References Examples

Description

Test for differential proportions of zero expression between two conditions for a specified set of genes; adapted from the R/Bioconductor package scDD by Korthauer et al. (2016)

Usage

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testZeroes(x, y, these = seq_len(nrow(x)))

## S4 method for signature 'matrix,vector,ANY'
testZeroes(x, y, these = seq_len(nrow(x)))

## S4 method for signature 'SingleCellExperiment,SingleCellExperiment,vector'
testZeroes(x, y, these = seq_len(nrow(x)))

Arguments

x

matrix of single-cell RNA-sequencing expression data with genes in rows and cells (samples) in columns [alternatively, a SingleCellExperiment object for condition A, where the matrix of the single-cell RNA sequencing expression data has to be supplied via the counts argument in SingleCellExperiment]

y

vector of condition labels [alternatively, a SingleCellExperiment object for condition B, where the matrix of the single-cell RNA sequencing expression data has to be supplied via the counts argument in SingleCellExperiment]

these

vector of row numbers (i.e. gene numbers) employed to test for differential proportions of zero expression; default is seq_len(nrow(dat))

Details

Test for differential proportions of zero gene expression between two conditions using a logistic regression model accounting for the cellular detection rate. Adapted from the R/Bioconductor package scDD by Korthauer et al. (2016).

In the test, the null hypothesis that there are no differential proportions of zero gene expression (DPZ) is tested against the alternative that there are DPZ.

Value

A vector of (unadjusted) p-values

References

Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., and Kendziorski, C. (2016). A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology, 17:222.

Examples

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#simulate scRNA-seq data
set.seed(24)
nb.sim1<-rnbinom(n=(750*250),1,0.7)
dat1<-matrix(data=nb.sim1,nrow=750,ncol=250)
nb.sim2a<-rnbinom(n=(250*100),1,0.7)
dat2a<-matrix(data=nb.sim2a,nrow=250,ncol=100)
nb.sim2b<-rnbinom(n=(250*150),5,0.2)
dat2b<-matrix(data=nb.sim2b,nrow=250,ncol=150)
dat2<-cbind(dat2a,dat2b)
dat<-rbind(dat1,dat2)*0.25
#randomly shuffle the rows of the matrix to create the input matrix
set.seed(32)
dat<-dat[sample(nrow(dat)),]
condition<-c(rep("A",100),rep("B",150))

#call testZeroes with a matrix and a vector including conditions
#test for differential proportions of zero expression over all rows (genes)
testZeroes(dat, condition)
#test for differential proportions of zero expression only for the second row (gene)
testZeroes(dat, condition, these=2)

#alternatively, call testZeroes with two SingleCellExperiment objects
#note that the possibly pre-processed and normalized expression matrices need to be
#included using the "counts" argument
sce.A <- SingleCellExperiment::SingleCellExperiment(
  assays=list(counts=dat[,1:100]))
sce.B <- SingleCellExperiment::SingleCellExperiment(
  assays=list(counts=dat[,101:250]))
#test for differential proportions of zero expression over all rows (genes)
testZeroes(sce.A,sce.B,these=seq_len(nrow(sce.A)))
#test for differential proportions of zero expression only for the second row (gene)
testZeroes(sce.A,sce.B,these=2)

goncalves-lab/waddR documentation built on Oct. 28, 2021, 5:50 a.m.